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市场调查报告书
商品编码
1372888

精准医疗市场中的人工智慧，2028-全球产业规模、份额、趋势、机会和预测，2018-2028 按技术、组件、治疗应用、地区、竞争细分

Artificial Intelligence In Precision Medicine Market, 2028- Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028 Segmented By Technology, By Component, By Therapeutic Application, By Region, By Competition
出版日期: | 出版商: TechSci Research | 英文 190 Pages | 商品交期: 2-3个工作天内

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简介目录

到 2022 年，全球人工智慧精准医疗市场价值将达到 12.4 亿美元，预计到 2028 年，复合年增长率将达到 21.13%。在不断发展的医疗保健领域，强大的融合正在发生。人工智慧（AI）和精准医疗之间的关係。这种突破性的协同作用有可能改变医疗治疗的发展、提供和个人化的方式。精准医疗市场中的全球人工智慧处于这项典范转移的最前沿，让我们得以一窥医疗保健创新的未来。精准医学的特点是根据每位患者的个别特征制定医疗治疗和介入措施，近年来获得了相当大的关注。这种方法承认患者之间固有的多样性，并考虑遗传、环境和生活方式等因素。同时，机器学习和深度学习等人工智慧技术在分析大量资料和提取可行见解方面表现出了卓越的能力。这两个领域的融合为优化诊断、治疗选择和患者结果带来了巨大的希望。

主要市场驱动因素

市场概况
预测期 2024-2028
2022 年市场规模 12.4亿美元
2028 年市场规模 39.2亿美元
2023-2028 年复合年增长率 21.13%
成长最快的细分市场 肿瘤学
最大的市场 北美洲

慢性病盛行率上升正在推动全球人工智慧在精准医疗市场的发展

慢性病通常称为非传染性疾病 (NCD)，涵盖多种健康状况，例如心血管疾病、糖尿病、癌症和呼吸道疾病。它们的特点是持续时间长、进展缓慢，并且需要持续的医疗护理和管理。根据世界卫生组织 (WHO) 的数据，全球死亡人数中近 71% 是由慢性病造成的，其中高达 85% 的死亡发生在低收入和中等收入国家。慢性病对社会经济的影响是深远的，它给医疗保健系统带来压力，降低劳动生产力，并降低个人及其家庭的生活品质。人工智慧，特别是机器学习和深度学习技术，已被证明是医疗保健产业的变革力量。人工智慧具有处理和分析海量资料集、识别复杂模式并产生预测模型的能力。当应用于精准医疗时，人工智慧可以挖掘基因组成、疾病易感性和治疗结果之间复杂的关係，从而实现更准确的诊断和个人化的治疗介入。人工智慧在精准医学中的重要应用之一是基因体学研究。人工智慧演算法可以快速分析患者的遗传讯息，并识别与某些疾病相关的特定突变或生物标记。这些资讯有助于临床医生就治疗策略做出明智的决定，使他们能够选择更有可能有效的药物并最大限度地减少不良反应。人工智慧驱动的工具也正在彻底改变医学影像分析。这些工具可以快速解读 X 光、MRI 和 CT 扫描等影像，有助于早期发现和诊断癌症、心臟病和神经退化性疾病等各种疾病。此外，人工智慧驱动的预测模型可以预测疾病进展，使医生能够主动干预并相应地制定治疗计划。人工智慧与精准医疗的融合带来了市场的快速扩张。根据市场研究报告，全球人工智慧精准医疗市场预计在未来几年将大幅成长。研究和开发资金的增加、人工智慧和医疗保健公司之间的合作伙伴关係不断加强以及对个人化治疗的需求不断增加等因素正在推动这一趋势。

随着科技的不断进步，人工智慧在精准医疗中的应用可能会进一步扩大。电子健康记录、穿戴式装置和即时监测的整合将为人工智慧演算法提供连续的资料流进行分析，从而能够及时干预和调整治疗计划。此外，人工智慧可以帮助发现新的药物标靶和开发创新的治疗干预措施，开创精准医疗的新时代。

药物发现和开发的激增推动了全球人工智慧在精准医学领域的成长

在药物发现和开发领域一直是一个复杂且耗时的过程。研究人员花费数年时间来识别潜在的候选药物，测试它们的安全性和有效性，然后经过漫长的监管审批过程，然后最终到达患者手中。然而，最近的技术进步，特别是人工智慧 (AI) 领域的进步，正在彻底改变药物的发现和开发方式。这在精准医疗领域不断成长的全球人工智慧市场中尤其明显。精准医疗，也称为个人化医疗，是一种创新的医疗保健方法，考虑到每个人基因、环境和生活方式的个体差异。透过根据每位患者的独特特征制定医疗治疗和干预措施，精准医疗旨在实现更好的结果，减少不良反应，并最终改善患者护理。人工智慧在推动精准医疗市场方面发挥着重要作用。人工智慧演算法可以分析大量患者资料，包括遗传资讯、病史和生活方式因素，以识别潜在的药物标靶并预测患者对不同治疗的反应。这加速了药物发现过程，使其更快、更有效率。

人工智慧产生重大影响的一个领域是识别潜在的候选药物。传统的药物发现方法通常涉及筛选大型化合物库，这可能既耗时又昂贵。另一方面，人工智慧演算法可以快速分析大量资料，识别潜在的药物标靶并预测哪些化合物可能具有治疗效果。此外，人工智慧也被用来预测患者对不同治疗的反应。透过分析患者资料，人工智慧演算法可以识别生物标誌物，帮助预测哪些患者更有可能对特定治疗产生反应，从而实现更有针对性和个人化的干预措施。

这一增长的主要驱动力是可用于分析的资料量不断增加。基因组定序技术的进步带来了遗传资料的爆炸性增长，为研究人员提供了有关疾病根本原因的宝贵见解。人工智慧演算法可以筛选这些资料，识别潜在的药物标靶并预测患者的反应。此外，製药公司和科技公司之间的合作正在进一步推动人工智慧在精准医疗市场的成长。这些合作伙伴关係正在推动创新人工智慧驱动工具和平台的开发，从而加速药物发现和开发流程。

主要市场挑战

数据品质和可近性对市场扩张构成重大障碍

人工智慧驱动的精准医疗市场面临的主要挑战之一是需要高品质、多样化和全面的医疗资料。人工智慧演算法严重依赖大型资料集来做出准确的预测和建议。然而，医疗保健资料通常分散在各种来源中，包括电子健康记录、基因组资料、穿戴式装置等。整合这些不同的资料来源，同时确保其准确性和安全性仍然是一项艰鉅的挑战。

资料隐私和安全

由于精准医疗中的人工智慧应用需要存取敏感的患者资料，因此对资料隐私和安​​全性的担忧已成为人们关注的焦点。平衡人工智慧驱动的洞察力的好处与患者机密性和资料保护法规是一个重大障碍。在用于研究目的的资料共享和维护患者信任之间取得适当的平衡对于市场的可持续成长至关重要。

缺乏标准化

将人工智慧融入精准医疗涉及整合多个来源的复杂资料以及开发分析演算法。医疗保健系统和机构之间缺乏标准化资料格式和互通性标准，对无缝资料共享和协作构成了巨大障碍。努力建立通用资料标准对于促进资讯交流和促进创新至关重要。

演算法偏差和可解释性

人工智慧演算法可能会无意中使训练资料中存在的偏见永久化，从而导致医疗结果的差异。在精准医学中，有偏见的演算法可能会导致不准确的诊断或治疗，特别是对于代表性不足的人群。此外，一些人工智慧模型的「黑盒子」性质为理解如何做出决策带来了挑战，限制了它们的临床接受度。努力建立透明且可解释的人工智慧模型对于在医疗保健提供者和患者之间建立信任至关重要。

临床验证和监管

为了让人工智慧驱动的精准医疗解决方案广泛接受，它们必须经过严格的临床验证，以证明其安全性、有效性和可靠性。基于人工智慧的医疗产品获得监管部门批准是一个复杂的过程，需要遵循不断发展的指导方针并展示现实世界的影响。将人工智慧精准医疗技术推向市场时，平衡创新与病患安全仍然是一个重大障碍。

融入临床工作流程

将人工智慧解决方案实施到现有的临床工作流程中可能具有挑战性。医疗保健专业人员已经被资讯淹没，在不破坏既定流程的情况下无缝整合新技术至关重要。提供使用者友善的介面、确保最小的干扰并展示切实的好处对于鼓励采用至关重要。

成本和资源限制

虽然人工智慧在精准医疗领域的潜在长期效益是巨大的，但技术实施和培训所需的初始投资可能也很大。许多医疗机构，尤其是在资源有限的环境中，可能会发现为人工智慧计画分配资金具有挑战性。展示经济价值和投资回报对于克服这些与成本相关的障碍至关重要。

主要市场趋势

技术进步

传统上，医疗和介入措施遵循一刀切的方法，但由于基因组成、生活方式和环境因素的个体差异，往往会导致效果不佳。另一方面，精准医学透过根据每位患者的特定特征制定医疗决策和介入措施，拥抱每位患者的独特性。基因组学、分子生物学和个人化诊断的进步使这种方法成为可能。分析大量患者资料（包括遗传资讯、病史和生活方式因素）的复杂性需要能够有效筛选这些资料并提取有意义的见解的工具。这就是人工智慧介入的地方，它提供理解复杂的患者资讯网路所需的运算能力和演算法智慧。精准医学中的人工智慧涉及利用机器学习演算法和深度学习技术来识别大型资料集中的模式、相关性和关联性。这些模式可能与疾病风险、治疗反应、药物交互作用等有关。人工智慧演算法接触的资料越多，它们就越能辨识出可能逃避人类分析的微妙连结。

医疗记录的数位化以及穿戴式装置和医疗感测器的爆炸性增长导致患者资料达到前所未有的水平。人工智慧演算法在资料上蓬勃发展，这些丰富的资讯使它们能够做出更准确的预测和建议。基因组学领域在破解人类基因组和了解疾病的遗传基础方面取得了显着进展。人工智慧可以帮助解释大量的遗传资讯并将其与临床结果联繫起来。人工智慧驱动的模拟和虚拟药物筛选可以加快药物发现和开发，从而创建与患者独特基因谱相匹配的标靶疗法。人工智慧技术可以加速医疗资料分析，从而加快诊断速度、优化治疗计划并缩短住院时间。这不仅可以改善患者的治疗效果，还可以降低医疗成本。

细分市场洞察

技术洞察

基于该技术，深度学习领域将在 2022 年成为全球精准医疗人工智慧市场的主导者。这可以归因于精准医疗旨在根据个别特征量身定制医疗和干预措施，从而使更有效和个性化的护理。深度学习是机器学习的子集，已被证明非常适合解决该领域的复杂问题。精准医学涉及分析大量异质资料，包括基因组学、蛋白质组学、医学影像、电子健康记录等。深度学习模型，特别是神经网络，擅长从如此多样化和高维度的资料类型中学习复杂的模式和表示。深度学习的关键优势之一是它能够从原始资料中自动提取相关特征。在精准医学中，可能无法明确定义有意义的特征，深度学习模型可以识别有助于疾病诊断、预后和治疗的微妙关係和特征。许多疾病都有复杂的潜在机制，其运作的复杂程度各不相同。深度学习的分层架构具有多层互连的神经元，可以捕捉这些复杂的模式和关係，使其非常适合对复杂的疾病过程进行建模。

组件洞察

预计软体领域将在预测期内经历快速成长。精准医学在很大程度上依赖分析大量患者资料，包括基因组、临床和生活方式资讯。人工智慧演算法能够处理这些复杂的资料集并提取有意义的见解。软体应用程式支援这些演算法的开发和部署，使医疗保健专业人员能够以手动方式无法实现的规模和复杂性分析患者资料。机器学习和深度学习模型等人工智慧演算法对于理解精准医学资料至关重要。这些演算法需要大量标记资料进行训练、微调和验证。软体平台为研究人员和资料科学家提供了有效设计、开发和训练这些人工智慧模型的基础设施。

区域洞察

2022年，北美成为全球精准医疗人工智慧市场的主导者，以价值计算，占据最大的市场份额。北美拥有先进的医疗基础设施，包括完善的电子健康记录 (EHR) 系统，该系统提供了大量的患者资料，可用于训练和验证精准医疗的人工智慧演算法。获取高品质资料对于开发准确的人工智慧模型至关重要。该地区为人工智慧新创公司和精准医疗领域的公司提供了大量投资和资金。创投公司和投资者被人工智慧与医疗保健相结合、推动市场创新和成长的潜力所吸引。北美，特别是美国，在人工智慧和医学领域拥有强大的研究和创新生态系统。该地区领先的研究型大学、医疗机构和科技公司一直处于开发精准医疗应用人工智慧技术的前沿。北美有着医疗保健和技术领域合作的传统。此次合作促进了人工智慧解决方案融入医疗实践。医院、研究机构和科技公司之间的合作加速了人工智慧驱动的精准医疗工具的开发和采用。

目录

第 1 章：产品概述

第 2 章：研究方法

第 3 章：执行摘要

第 4 章：客户之声

第 5 章：全球人工智慧精准医疗市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按技术（软体解决方案、硬体、服务）
    • 依癌症类型（乳癌、肺癌、摄护腺癌、大肠癌、脑肿瘤、其他）
    • 按最终使用者（医院、外科中心和医疗机构、其他）
    • 按地区
    • 按公司划分 (2022)
  • 市场地图

第 6 章：北美人工智慧精准医疗市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依技术
    • 按癌症类型
    • 按最终用户
    • 按形式
    • 按配销通路
    • 按国家/地区
  • 北美：国家分析
    • 美国
    • 加拿大
    • 墨西哥

第 7 章：欧洲人工智慧精准医疗市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依技术
    • 按癌症类型
    • 按最终用户
  • 欧洲：国家分析
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙

第 8 章：亚太地区人工智慧精准医疗市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依技术
    • 按癌症类型
    • 按最终用户
  • 亚太地区：国家分析
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳洲

第 9 章：南美洲人工智慧在精准医疗市场前景

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依技术
    • 按癌症类型
    • 按最终用户
  • 南美洲：国家分析
    • 巴西
    • 阿根廷
    • 哥伦比亚

第 10 章：中东和非洲精准医疗中的人工智慧市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依技术
    • 按癌症类型
    • 按最终用户
  • MEA：国家分析
    • 南非 精准医疗中的人工智慧
    • 沙乌地阿拉伯人工智慧在精准医学的应用
    • 阿联酋人工智慧在精准医疗的应用

第 11 章：市场动态

第 12 章：市场趋势与发展

第 13 章：精准医疗市场中的全球人工智慧：SWOT 分析

第14章：竞争格局

  • 商业概览
  • 癌症类型产品
  • 最近的发展
  • 主要人员
  • SWOT分析
    • Medial EarlySign
    • Cancer Center.ai
    • Microsoft Corporation
    • Flatiron Health
    • Path AI
    • Therapixel
    • Tempus Labs, Inc.
    • Paige AI, Inc.
    • Kheiron Medical Technologies Limited
    • SkinVision

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简介目录
Product Code: 16237

Global Artificial Intelligence In Precision Medicine Market has valued at USD 1.24 billion in 2022 and is anticipated to project impressive growth in the forecast period with a CAGR of 21.13% through 2028. In the ever-evolving landscape of healthcare, a powerful convergence is taking place between artificial intelligence (AI) and precision medicine. This groundbreaking synergy has the potential to transform the way medical treatments are developed, delivered, and personalized. The Global Artificial Intelligence in Precision Medicine Market is at the forefront of this paradigm shift, offering a glimpse into the future of healthcare innovation. Precision medicine, characterized by tailoring medical treatments and interventions to the individual characteristics of each patient, has gained considerable traction in recent years. This approach acknowledges the inherent diversity among patients, taking into account factors such as genetics, environment, and lifestyle. Meanwhile, AI technologies like machine learning and deep learning have demonstrated remarkable capabilities in analyzing vast amounts of data and extracting actionable insights. The amalgamation of these two domains holds immense promise for optimizing diagnosis, treatment selection, and patient outcomes.

Traditional one-size-fits-all medical approaches are gradually making way for personalized treatments. Patients and healthcare providers alike are recognizing the potential of AI to unlock the intricacies of individual health profiles, enabling tailored therapies. The decreasing cost of genomic sequencing has led to an explosion of genetic data. AI algorithms can swiftly sift through this information, identifying genetic markers associated with diseases, and paving the way for targeted interventions. The digitization of healthcare records and the proliferation of wearable devices have generated an unprecedented volume of patient data. AI can aggregate, analyse, and integrate these diverse data sources, yielding comprehensive insights that were previously unattainable. AI is revolutionizing the drug discovery process by predicting potential drug candidates, simulating drug interactions, and expediting preclinical testing. This not only reduces costs but also accelerates the delivery of innovative therapies to market.

Key Market Drivers

Market Overview
Forecast Period2024-2028
Market Size 2022USD 1.24 Billion
Market Size 2028USD 3.92 Billion
CAGR 2023-202821.13%
Fastest Growing SegmentOncology
Largest MarketNorth America

Rising Prevalence of Chronic Diseases is Driving the Global Artificial Intelligence In Precision Medicine Market

Chronic diseases, often referred to as non-communicable diseases (NCDs), encompass a wide range of health conditions such as cardiovascular diseases, diabetes, cancer, and respiratory illnesses. They are characterized by their prolonged duration, slow progression, and the requirement for ongoing medical attention and management. According to the World Health Organization (WHO), chronic diseases are responsible for almost 71% of all global deaths, with a staggering 85% of these deaths occurring in low- and middle-income countries. The socioeconomic impact of chronic diseases is profound, straining healthcare systems, reducing workforce productivity, and diminishing the quality of life for individuals and their families. Artificial Intelligence, specifically machine learning and deep learning techniques, has proven to be a transformative force in the healthcare industry. AI has the ability to process and analyze massive datasets, recognize complex patterns, and generate predictive models. When applied to precision medicine, AI can mine intricate relationships between genetic makeup, disease susceptibility, and treatment outcomes, leading to more accurate diagnoses and personalized therapeutic interventions. One of the significant applications of AI in precision medicine is in genomics research. AI algorithms can swiftly analyze a patient's genetic information and identify specific mutations or biomarkers associated with certain diseases. This information aids clinicians in making informed decisions about treatment strategies, enabling them to select medications that are more likely to be effective and minimize adverse effects. AI-powered tools are also revolutionizing medical imaging analysis. These tools can rapidly interpret images such as X-rays, MRIs, and CT scans, aiding in the early detection and diagnosis of various conditions like cancer, heart disease, and neurodegenerative disorders. Additionally, AI-driven predictive models can forecast disease progression, allowing physicians to intervene proactively and tailor treatment plans accordingly. The convergence of AI and precision medicine has resulted in a rapidly expanding market. According to market research reports, the Global Artificial Intelligence in Precision Medicine Market is projected to experience substantial growth over the coming years. Factors such as increased funding for research and development, growing partnerships between AI and healthcare companies, and the escalating demand for personalized treatments are driving this trend.

As technology continues to advance, the applications of AI in precision medicine will likely expand further. Integration of electronic health records, wearable devices, and real-time monitoring will provide a continuous stream of data for AI algorithms to analyze, enabling timely interventions and adjustments to treatment plans. Moreover, AI can aid in the discovery of novel drug targets and the development of innovative therapeutic interventions, ushering in a new era of precision medicine.

The Surge of Drug Discovery and Development Fuels Growth in Global Artificial Intelligence in Precision Medicine

In The field of drug discovery and development has always been a complex and time-consuming process. Researchers spend years identifying potential drug candidates, testing them for safety and efficacy, and then going through a lengthy regulatory approval process before they can finally reach patients. However, recent advancements in technology, particularly in the field of artificial intelligence (AI), are revolutionizing the way drugs are discovered and developed. This is particularly evident in the rising global market for AI in precision medicine. Precision medicine, also known as personalized medicine, is an innovative approach to healthcare that takes into account individual variability in genes, environment, and lifestyle for each person. By tailoring medical treatment and interventions to the unique characteristics of each patient, precision medicine aims to achieve better outcomes, reduce adverse effects, and ultimately improve patient care. Artificial intelligence has found a significant role in driving the precision medicine market. AI algorithms can analyze vast amounts of patient data, including genetic information, medical history, and lifestyle factors, to identify potential drug targets and predict how patients will respond to different treatments. This accelerates the drug discovery process, making it faster and more efficient.

One area where AI is making a considerable impact is in identifying potential drug candidates. Traditional methods of drug discovery often involve screening large libraries of chemical compounds, which can be time-consuming and expensive. AI algorithms, on the other hand, can quickly analyze vast amounts of data to identify potential drug targets and predict which compounds are likely to have a therapeutic effect. Additionally, AI is also being used to predict how patients will respond to different treatments. By analyzing patient data, AI algorithms can identify biomarkers that can help predict which patients are more likely to respond to a specific treatment, allowing for more targeted and personalized interventions.

One major driver of this growth is the increasing amount of data available for analysis. Advances in genomic sequencing technology have led to an explosion of genetic data, providing researchers with valuable insights into the underlying causes of diseases. AI algorithms can sift through this data to identify potential drug targets and predict patient responses.In addition, collaborations between pharmaceutical companies and technology firms are further propelling the growth of the AI in precision medicine market. These partnerships are enabling the development of innovative AI-driven tools and platforms that can accelerate drug discovery and development processes.

Key Market Challenges

Data Quality and Accessibility Poses a Significant Obstacle To Market Expansion

One of the primary challenges facing the AI-driven precision medicine market is the need for high-quality, diverse, and comprehensive healthcare data. AI algorithms rely heavily on large datasets to make accurate predictions and recommendations. However, healthcare data is often fragmented across various sources, including electronic health records, genomic data, wearable devices, and more. Integrating these disparate data sources while ensuring their accuracy and security remains a formidable challenge.

Data Privacy and Security

As AI applications in precision medicine require access to sensitive patient data, concerns about data privacy and security have come to the forefront. Balancing the benefits of AI-driven insights with patient confidentiality and data protection regulations is a significant hurdle. Striking the right balance between data sharing for research purposes and maintaining patient trust is crucial for the sustainable growth of the market.

Lack of Standardization

Incorporating AI into precision medicine involves the integration of complex data from multiple sources and the development of algorithms for analysis. The lack of standardized data formats and interoperability standards across healthcare systems and institutions poses a substantial barrier to seamless data sharing and collaboration. Efforts to establish common data standards are essential to facilitate the exchange of information and foster innovation.

Algorithm Bias and Interpretability

AI algorithms can inadvertently perpetuate biases present in training data, leading to disparities in healthcare outcomes. In precision medicine, biased algorithms could result in inaccurate diagnoses or treatments, particularly for underrepresented populations. Additionally, the "black box" nature of some AI models poses challenges in understanding how decisions are reached, limiting their clinical acceptance. Striving for transparent and interpretable AI models is crucial for building trust among healthcare providers and patients.

Clinical Validation and Regulation

For AI-driven precision medicine solutions to gain widespread acceptance, they must undergo rigorous clinical validation to demonstrate their safety, efficacy, and reliability. Achieving regulatory approval for AI-based medical products is a complex process that requires navigating evolving guidelines and demonstrating real-world impact. Balancing innovation with patient safety remains a significant hurdle in bringing AI-enabled precision medicine technologies to market.

Integration into Clinical Workflow

Implementing AI solutions into the existing clinical workflow can be challenging. Healthcare professionals are already inundated with information, and integrating new technologies seamlessly without disrupting established processes is crucial. Providing user-friendly interfaces, ensuring minimal disruption, and demonstrating tangible benefits are essential to encourage adoption.

Cost and Resource Constraints

While the potential long-term benefits of AI in precision medicine are substantial, the initial investment required for technology implementation and training can be significant. Many healthcare institutions, especially in resource-constrained environments, might find it challenging to allocate funds for AI initiatives. Demonstrating the economic value and return on investment is crucial to overcoming these cost-related barriers.

Key Market Trends

Technological Advancements

Traditionally, medical treatments and interventions have followed a one-size-fits-all approach, often resulting in suboptimal outcomes due to individual variations in genetic makeup, lifestyle, and environmental factors. Precision medicine, on the other hand, embraces the uniqueness of each patient by tailoring medical decisions and interventions based on their specific characteristics. This approach has been made possible by advances in genomics, molecular biology, and personalized diagnostics. The complexity of analyzing vast amounts of patient data, including genetic information, medical histories, and lifestyle factors, requires tools that can sift through this data efficiently and extract meaningful insights. This is where artificial intelligence steps in, offering the computational power and algorithmic intelligence needed to make sense of the intricate web of patient information. AI in precision medicine involves the utilization of machine learning algorithms and deep learning techniques to identify patterns, correlations, and associations within large datasets. These patterns could relate to disease risk, treatment response, drug interactions, and more. The more data AI algorithms are exposed to, the better they become at identifying subtle connections that might elude human analysis.

The digitalization of healthcare records, along with the explosion of wearable devices and medical sensors, has led to an unprecedented volume of patient data. AI algorithms thrive on data, and this wealth of information enables them to make more accurate predictions and recommendations. The field of genomics has seen remarkable progress in deciphering the human genome and understanding the genetic basis of diseases. AI can aid in interpreting this vast genetic information and linking it to clinical outcomes. AI-driven simulations and virtual drug screening can expedite drug discovery and development, allowing for the creation of targeted therapies that are aligned with a patient's unique genetic profile. AI technologies can accelerate the analysis of medical data, leading to quicker diagnoses, optimized treatment plans, and shorter hospital stays. This not only improves patient outcomes but also reduces healthcare costs.

Segmental Insights

Technology Insights

Based on the Technology, the Deep Learning segment emerged as the dominant player in the global market for Artificial Intelligence In Precision Medicine in 2022. This can be attributed to the fact that precision medicine aims to tailor medical treatment and interventions to individual characteristics, allowing for more effective and personalized care. Deep Learning, a subset of machine learning, has proven to be exceptionally well-suited for solving complex problems in this field. Precision medicine involves analyzing a vast amount of heterogeneous data, including genomics, proteomics, medical images, electronic health records, and more. Deep Learning models, particularly neural networks, excel at learning intricate patterns and representations from such diverse and high-dimensional data types. One of the key strengths of Deep Learning is its ability to automatically extract relevant features from raw data. In precision medicine, where meaningful features might not be explicitly defined, Deep Learning models can identify subtle relationships and features that contribute to disease diagnosis, prognosis, and treatment. Many diseases have intricated underlying mechanisms that operate at various levels of complexity. Deep Learning's hierarchical architecture, with multiple layers of interconnected neurons, can capture these intricate patterns and relationships, making it well-suited for modeling complex disease processes.

Component Insights

The software segment is projected to experience rapid growth during the forecast period. Precision medicine relies heavily on analyzing vast amounts of patient data, including genomic, clinical, and lifestyle information. AI algorithms are capable of processing and extracting meaningful insights from these complex datasets. Software applications enable the development and deployment of these algorithms, allowing healthcare professionals to analyze patient data at a scale and complexity that would be impossible manually. AI algorithms, such as machine learning and deep learning models, are central to making sense of precision medicine data. These algorithms require large amounts of labeled data for training, fine-tuning, and validation. Software platforms provide the infrastructure for researchers and data scientists to design, develop, and train these AI models effectively.

Regional Insights

North America emerged as the dominant player in the global Artificial Intelligence In Precision Medicine market in 2022, holding the largest market share in terms of value. North America boasts advanced healthcare infrastructure, including well-established electronic health record (EHR) systems, which provide a wealth of patient data that can be used to train and validate AI algorithms for precision medicine. Access to high-quality data is crucial for developing accurate AI models. The region has witnessed substantial investments and funding for AI startups and companies working in the field of precision medicine. Venture capital firms and investors are drawn to the potential of combining AI with healthcare, driving innovation and growth in the market. North America, particularly the United States, has a robust ecosystem for research and innovation in both AI and medicine. Leading research universities, medical institutions, and technology companies in the region have been at the forefront of developing AI technologies for precision medicine applications. North America has a tradition of collaboration between the healthcare and technology sectors. This collaboration has facilitated the integration of AI solutions into medical practice. Partnerships between hospitals, research institutions, and tech companies have accelerated the development and adoption of AI-powered precision medicine tools.

Key Market Players

  • Glanbia Plc
  • BioXcel Therapeutics, Inc.
  • Sanofi S.A.
  • NVIDIA Corp.
  • Alphabet Inc. (Google Inc.)
  • IBM Technology corporation
  • Microsoft Corporation
  • Intel Corp.
  • AstraZeneca plc
  • GE HealthCare
  • Enlitic, Inc.

Report Scope:

In this report, the Global Artificial Intelligence In Precision Medicine Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Artificial Intelligence In Precision Medicine Market, By Technology:

  • Deep Learning
  • Querying Method
  • Natural Language Processing

Artificial Intelligence In Precision Medicine Market, By Component :

  • Hardware
  • Software
  • Service

Artificial Intelligence In Precision Medicine Market, By Therapeutic Application :

  • Oncology
  • Cardiology
  • Neurology
  • Respiratory
  • Other

Artificial Intelligence In Precision Medicine Market, By Region:

  • North America
  • United States
  • Canada
  • Mexico
  • Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
  • Asia-Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • South America
  • Brazil
  • Argentina
  • Colombia
  • Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

  • Company Profiles: Detailed analysis of the major companies present in the Global Artificial Intelligence In Precision Medicine Market.

Available Customizations:

  • Global Artificial Intelligence In Precision Medicine market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

2. Research Methodology

3. Executive Summary

4. Voice of Customer

5. Global Artificial Intelligence In Precision Medicine Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Technology (Software Solutions, Hardware, Services)
    • 5.2.2. By Cancer Type (Breast Cancer, Lung Cancer, Prostate Cancer, Colorectal Cancer, Brain Tumor, Others)
    • 5.2.3. By End-User (Hospital, Surgical Centers and Medical Institutes, Others)
    • 5.2.4. By Region
    • 5.2.5. By Company (2022)
  • 5.3. Market Map

6. North America Artificial Intelligence In Precision Medicine Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Technology
    • 6.2.2. By Cancer Type
    • 6.2.3. By End-User
    • 6.2.4. By Form
    • 6.2.5. By Distribution Channel
    • 6.2.6. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Artificial Intelligence In Precision Medicine Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Technology
        • 6.3.1.2.2. By Cancer Type
        • 6.3.1.2.3. By End-User
    • 6.3.2. Canada Artificial Intelligence In Precision Medicine Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Technology
        • 6.3.2.2.2. By Cancer Type
        • 6.3.2.2.3. By End-User
    • 6.3.3. Mexico Artificial Intelligence In Precision Medicine Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Technology
        • 6.3.3.2.2. By Cancer Type
        • 6.3.3.2.3. By End-User

7. Europe Artificial Intelligence In Precision Medicine Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Technology
    • 7.2.2. By Cancer Type
    • 7.2.3. By End-User
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Technology
        • 7.3.1.2.2. By Cancer Type
        • 7.3.1.2.3. By End-User
    • 7.3.2. United Kingdom Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Technology
        • 7.3.2.2.2. By Cancer Type
        • 7.3.2.2.3. By End-User
    • 7.3.3. Italy Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecasty
        • 7.3.3.2.1. By Technology
        • 7.3.3.2.2. By Cancer Type
        • 7.3.3.2.3. By End-User
    • 7.3.4. France Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Technology
        • 7.3.4.2.2. By Cancer Type
        • 7.3.4.2.3. By End-User
    • 7.3.5. Spain Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Technology
        • 7.3.5.2.2. By Cancer Type
        • 7.3.5.2.3. By End-User

8. Asia-Pacific Artificial Intelligence In Precision Medicine Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Technology
    • 8.2.2. By Cancer Type
    • 8.2.3. By End-User
  • 8.3. Asia-Pacific: Country Analysis
    • 8.3.1. China Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Technology
        • 8.3.1.2.2. By Cancer Type
        • 8.3.1.2.3. By End-User
    • 8.3.2. India Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Technology
        • 8.3.2.2.2. By Cancer Type
        • 8.3.2.2.3. By End-User
    • 8.3.3. Japan Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Technology
        • 8.3.3.2.2. By Cancer Type
        • 8.3.3.2.3. By End-User
    • 8.3.4. South Korea Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Technology
        • 8.3.4.2.2. By Cancer Type
        • 8.3.4.2.3. By End-User
    • 8.3.5. Australia Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Technology
        • 8.3.5.2.2. By Cancer Type
        • 8.3.5.2.3. By End-User

9. South America Artificial Intelligence In Precision Medicine Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Technology
    • 9.2.2. By Cancer Type
    • 9.2.3. By End-User
  • 9.3. South America: Country Analysis
    • 9.3.1. Brazil Artificial Intelligence In Precision Medicine Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Technology
        • 9.3.1.2.2. By Cancer Type
        • 9.3.1.2.3. By End-User
    • 9.3.2. Argentina Artificial Intelligence In Precision Medicine Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Technology
        • 9.3.2.2.2. By Cancer Type
        • 9.3.2.2.3. By End-User
    • 9.3.3. Colombia Artificial Intelligence In Precision Medicine Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Technology
        • 9.3.3.2.2. By Cancer Type
        • 9.3.3.2.3. By End-User

10. Middle East and Africa Artificial Intelligence In Precision Medicine Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Technology
    • 10.2.2. By Cancer Type
    • 10.2.3. By End-User
  • 10.3. MEA: Country Analysis
    • 10.3.1. South Africa Artificial Intelligence In Precision Medicine Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Technology
        • 10.3.1.2.2. By Cancer Type
        • 10.3.1.2.3. By End-User
    • 10.3.2. Saudi Arabia Artificial Intelligence In Precision Medicine Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Technology
        • 10.3.2.2.2. By Cancer Type
        • 10.3.2.2.3. By End-User
    • 10.3.3. UAE Artificial Intelligence In Precision Medicine Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Technology
        • 10.3.3.2.2. By Cancer Type
        • 10.3.3.2.3. By End-User

11. Market Dynamics

12. Market Trends & Developments

13. Global Artificial Intelligence In Precision Medicine Market: SWOT Analysis

14. Competitive Landscape

  • 14.1. Business Overview
  • 14.2. Cancer Type Offerings
  • 14.3. Recent Developments
  • 14.4. Key Personnel
  • 14.5. SWOT Analysis
    • 14.5.1. Medial EarlySign
    • 14.5.2. Cancer Center.ai
    • 14.5.3. Microsoft Corporation
    • 14.5.4. Flatiron Health
    • 14.5.5. Path AI
    • 14.5.6. Therapixel
    • 14.5.7. Tempus Labs, Inc.
    • 14.5.8. Paige AI, Inc.
    • 14.5.9. Kheiron Medical Technologies Limited
    • 14.5.10. SkinVision

15. Strategic Recommendations

16. About Us & Disclaimer